# The relationship between mathematics preparation and conceptual learning gains in physics: a possible hidden variable in diagnostic pretest scores

Save this PDF as:

Size: px
Start display at page:

Download "The relationship between mathematics preparation and conceptual learning gains in physics: a possible hidden variable in diagnostic pretest scores"

## Transcription

1 Addendum to: The relationship between mathematics preparation and conceptual learning gains in physics: a possible hidden variable in diagnostic pretest scores David E. Meltzer Department of Physics and Astronomy, Iowa State University, Ames, Iowa NORMALIZED LEARNING GAIN: A KEY MEASURE OF STUDENT LEARNING A single examination yields information about a student s knowledge state at one point in time. However, the primary interest of instructors is in learning, that is, the transition between states. In addition to being inadequate by itself for measuring that transition, performance on a single exam may be strongly correlated with a student s preinstruction preparation and knowledge. In order to assess learning per se it is necessary to have a measure that reflects the transition between knowledge states and that has maximum dependence on instruction, with minimum dependence on students preinstruction states. Therefore, it would be desirable to have a measure that is correlated with instructional activities, but that has minimum correlation with students pretest scores. In addition, the ideal measure would be reliable in the sense that minor differences in test instrument should yield approximately the same value of learning gain. The question of how to measure learning gain is not a simple one, and it is subject to many methodological difficulties. 1 Here I will simply identify some of the most significant issues. 1

2 To the extent that pre- and post-testing makes use of an instrument that has both a minimum and maximum possible score (as most do), there are issues of floor and ceiling effects. That is, a student can get no less than 0% nor more than 100% correct on such an instrument. This immediately leads to a problem in quantifying learning gain. For, a student whose pretest score is, say, 20% may gain 80 percentage points, while a student who begins at 90% may gain no more than ten. If the first student gains ten percentage points, and the second gains nine, one would be hard pressed to conclude that the first student has really learned more. Quite likely, a somewhat different assessment instrument might have led to a very different conclusion. (For instance, if the questions were significantly harder so that the two students pretest scores were 10% and 45%, respectively, the second student might very well have shown a much greater gain as measured simply in terms of percentage points.) As a consequence of the ceiling effect (and other reasons), it is common to observe a strong negative correlation between students absolute gain scores (posttest score minus pretest score) and their pretest score: higher pretest scores tend to result in smaller absolute gains, all else being equal. If, therefore, one wants a measure of learning gain that is reliable such that simply modifying the testing instrument would not lead to widely disparate results the absolute gain score is unlikely to suffice. The fact that the gain score tends to be correlated (negatively) with pretest scores is also an obstacle to isolating a measure of learning from confounding effects of preinstruction state. One way of dealing with this problem is to derive a measure that normalizes the gain score in a manner that takes some account of the variance in pretest scores. As is well known, the use of such a measure in 2

3 physics education research was introduced by Hake in Ref. 2; it is called the normalized gain g and it simply the absolute gain divided by the maximum possible gain: posttest score pretest score g = maximum possible score pretest score In Hake s original study he found that, for a sample of 62 high-school, college, and university physics courses enrolling over 6500 students, the mean normalized gain <g> for a given course on the Force Concept Inventory was almost completely uncorrelated (r = +0.02) with the mean pretest score of the students in that course. (In this case, mean normalized gain <g> is found by using the mean pretest score and mean posttest score of all students in the class.) At the very least, then, <g> seems to have the merit of being relatively independent of pretest score, and one might therefore expect that the reliability of whatever measure of learning gain it yields would not be threatened simply because a particular class had unusually high or low pretest scores. That is, if a diverse set of classes has a wide range of pretest scores but all other learning conditions are essentially identical, the values of normalized learning gain measured in the different classes should not differ significantly. The pretest-independence of normalized gain also suggests that a measurement of the <g> difference between two classes having very different pretest scores would be reproduced even by a somewhat different test instrument which results in a shifting of pretest scores. If one class had, say, a <g> = 0.80 and the other had <g> = 0.20, using another instrument which significantly alters their pretest scores might still be expected to yield more or less unchanged values of <g>. 3

4 By contrast, Hake found that the absolute gain scores were significantly (negatively) correlated with pretest score (r = 0.49). Suppose then that Class A (pretest score = 20%) had an absolute learning gain of 16 points, while Class B (pretest score = 90%) only had an eight-point gain, i.e., half as much as A. We would suspect that there might actually have been a greater learning gain in Class B, but that it was disguised by the ceiling effect on the test instrument. Suppose an assessment instrument that tested for the same concepts but with harder questions changed the pretest scores to 10% for A, and 45% for B. We might now find A with a gain of 18 points and B with a gain of 44 points and so now Class B is found to have the greater learning gain. Yet in this example, both classes would be found to have unchanged <g> values (<g> A = 0.20; <g> B = 0.80). Thus we can argue that <g> is the more reliable measure because the relationship it yields is reproduced with the use of a test instrument that differs from the original only in an insignificant manner. Now, this is a mock example with made-up numbers and untested assumptions. However it is consistent with the findings of Hake s survey and I would argue that it is a plausible scenario. Some empirical support might be drawn from the data reported at RPI (Ref. 3, Table II). For seven sections of the standard studio course, the mean pretest score and mean absolute gain score as measured on the FCI were 49.8% and 9.6%±2.2% (s.e.), respectively (s.e. standard error). The mean pretest score for the same seven sections as measured on the FMCE was lower (35.4%) and, as we might expect from the discussion above, the mean absolute gain score was indeed higher: 13.8%±1.4% (s.e.). These mean gain scores are significantly different according to a one-tailed, paired twosample t-test (t = 2.23, p = 0.03), and the higher FMCE gain might naively be interpreted 4

5 as a substantially higher learning gain (13.8/9.6 = 144%). However, despite the significantly different pretest scores, the mean normalized gains as determined by the two instruments were statistically indistinguishable: <g> FCI = 0.18±0.04(s.e.); <g> FMCE = 0.21±0.02(s.e.); (t = 0.84, p = 0.22). The FMCE and the FCI do not measure exactly the same concept knowledge and so one might argue that the measured learning gains really should be different. We have insufficient data to dispute that argument in this case and so this example should be seen as primarily illustrative. Probably the most persuasive empirical support for use of normalized gain as a reliable measure lies in the fact that <g> has now been measured for literally tens of thousands of students in many hundreds of classes worldwide with extremely consistent results. The values of <g> observed for both traditional courses and those taught using interactive-engagement methods both fall into relatively narrow bands which are reproduced with great regularity, for classes at a very broad range of institutions with widely varying student demographic characteristics (including pretest scores). 4 This provides a strong argument that normalized gain <g> is a valid and reliable measure of student learning gain. References 1 Walter R. Borg and Meredith D. Gall, Educational Research, An Introduction (Longman, New York, 1989), 5 th ed., pp Richard R. Hake, Interactive engagement versus traditional methods: A six-thousandstudent survey of mechanics test data for introductory physics courses, Am. J. Phys. 66, (1998); 5

6 3 Karen Cummings, Jeffrey Marx, Ronald Thornton and Dennis Kuhl, Evaluating innovations in studio physics, Phys. Educ. Res., Am. J. Phys. Suppl. 67, S38-S44 (1999). 4 Edward F. Redish and Richard N. Steinberg, Teaching physics: figuring out what works, Phys. Today 52, (1999); Richard R. Hake, "Lessons from the physics education reform effort," Conservation Ecology 5, 28 (2002), 6

### Real Time Physics and Interactive Lecture Demonstration Dissemination Project - Evaluation Report

Real Time Physics and Interactive Lecture Demonstration Dissemination Project - Evaluation Report Michael C. Wittmann Department of Physics University of Maryland College Park MD 20742-4111 I. Introduction

### The relationship between mathematics preparation and conceptual learning gains in physics: a possible hidden variable in diagnostic pretest scores

The relationship between mathematics preparation and conceptual learning gains in physics: a possible hidden variable in diagnostic pretest scores Abstract David E. Meltzer Department of Physics and Astronomy,

### 13 Two-Sample T Tests

www.ck12.org CHAPTER 13 Two-Sample T Tests Chapter Outline 13.1 TESTING A HYPOTHESIS FOR DEPENDENT AND INDEPENDENT SAMPLES 270 www.ck12.org Chapter 13. Two-Sample T Tests 13.1 Testing a Hypothesis for

### Effective use of wireless student response units ( clickers ) Dr. Douglas Duncan University of Colorado, Boulder dduncan@colorado.

Effective use of wireless student response units ( clickers ) Dr. Douglas Duncan University of Colorado, Boulder dduncan@colorado.edu I. How much do students learn in lectures? We ve been teaching the

### WILL TEACHING MATHEMATICS IN A PHYSICS CLASSROOM IMPROVE STUDENTS' ABILITIES TO DO MATHEMATICS IN A PHYSICS CLASSROOM AND TO LEARN PHYSICS?

WILL TEACHING MATHEMATICS IN A PHYSICS CLASSROOM IMPROVE STUDENTS' ABILITIES TO DO MATHEMATICS IN A PHYSICS CLASSROOM AND TO LEARN PHYSICS? Michael Murphy Committee Dr. Michael C. Wittmann, Advisor Dr.

### Evaluation Novelty in Modeling-Based and Interactive Engagement Instruction

Eurasia Journal of Mathematics, Science & Technology Education, 2007, 3(3), 231-237 Evaluation Novelty in Modeling-Based and Interactive Engagement Instruction Funda Örnek Balıkesir Üniversitesi, Balıkesir,

### Effect of Visualizations and Active Learning on Students Understanding of Electromagnetism Concepts

Effect of Visualizations and Active Learning on Students Understanding of Electromagnetism Concepts Yehudit Judy Dori Department of Education in Technology and Science Technion, Israel Institute of Technology,

### EXCEL Analysis TookPak [Statistical Analysis] 1. First of all, check to make sure that the Analysis ToolPak is installed. Here is how you do it:

EXCEL Analysis TookPak [Statistical Analysis] 1 First of all, check to make sure that the Analysis ToolPak is installed. Here is how you do it: a. From the Tools menu, choose Add-Ins b. Make sure Analysis

### Fixed-Effect Versus Random-Effects Models

CHAPTER 13 Fixed-Effect Versus Random-Effects Models Introduction Definition of a summary effect Estimating the summary effect Extreme effect size in a large study or a small study Confidence interval

### Chapter 9. Two-Sample Tests. Effect Sizes and Power Paired t Test Calculation

Chapter 9 Two-Sample Tests Paired t Test (Correlated Groups t Test) Effect Sizes and Power Paired t Test Calculation Summary Independent t Test Chapter 9 Homework Power and Two-Sample Tests: Paired Versus

### Research, Innovation and Reform in Physics Education

Research, Innovation and Reform in Physics Education David E. Meltzer Department of Physics and Astronomy Iowa State University Supported in part by the National Science Foundation Collaborator Mani K.

### An Introduction to Statistics Course (ECOE 1302) Spring Semester 2011 Chapter 10- TWO-SAMPLE TESTS

The Islamic University of Gaza Faculty of Commerce Department of Economics and Political Sciences An Introduction to Statistics Course (ECOE 130) Spring Semester 011 Chapter 10- TWO-SAMPLE TESTS Practice

### The Effect of Animation on Students' Responses to Conceptual Questions

The Effect of Animation on Students' Responses to Conceptual Questions Melissa Dancy: North Carolina State University mhdancy@unity.ncsu.edu Aaron Titus: North Carolina A&T State University titus@ncat.edu

### Measuring Evaluation Results with Microsoft Excel

LAURA COLOSI Measuring Evaluation Results with Microsoft Excel The purpose of this tutorial is to provide instruction on performing basic functions using Microsoft Excel. Although Excel has the ability

### Secondary Analysis of Teaching Methods in Introductory Physics: a 50k- Student Study

Secondary Analysis of Teaching Methods in Introductory Physics: a 50k- Student Study Joshua Von Korff* Department of Physics and Astronomy, Georgia State University, Atlanta, GA 30303 Benjamin Archibeque

### A Study in Engineering and Military Ethics

Abstract A Study in Engineering and Military Ethics Gayle Davis This paper was completed and submitted in partial fulfillment of the Master Teacher Program, a 2-year faculty professional development program

### Skepticism about the external world & the problem of other minds

Skepticism about the external world & the problem of other minds So far in this course we have, broadly speaking, discussed two different sorts of issues: issues connected with the nature of persons (a

### A + dvancer College Readiness Online Improves. ACCUPLACER Scores of Miami Dade College Freshmen. May 2008. John Vassiliou, M. S.

A + dvancer College Readiness Online Improves ACCUPLACER Scores of Miami Dade College Freshmen May 2008 John Vassiliou, M. S. Nicholas B. McDonald, Ph.D. The American Education Corporation EXECUTIVE SUMMARY

### An Experiment in the Use of Mobile Phones for Testing at King Mongkut s Institute of Technology North Bangkok, Thailand

An Experiment in the Use of Mobile Phones for Testing at King Mongkut s Institute of Technology North Bangkok, Thailand Krismant Whattananarong* Abstract The purposes of this study were to investigate

### SKILL LEVEL ASSESSMENT AND MULTI-SECTION STANDARDIZATION FOR AN INTRODUCTORY MICROCOMPUTER APPLICATIONS COURSE

SKILL LEVEL ASSESSMENT AND MULTI-SECTION STANDARDIZATION FOR AN INTRODUCTORY MICROCOMPUTER APPLICATIONS COURSE Dr. Diana I. Kline and Dr. Ted J. Strickland, Jr. Department of Computer Information Systems

ATTENTION: SUBJECT: Hands on Banking Instructors Pre- and Post-tests for Adults and Young Adults If you use the Hands on Banking Adults or Young Adults courses with a group, we invite you to use the attached

### Math Science Partnership (MSP) Program: Title II, Part B

Math Science Partnership (MSP) Program: Title II, Part B FLOYD COUNTY: COLLABORATIVE SYMPOSIUM FOR MATH IMPROVEMENT IN FLOYD COUNTY SCHOOLS ANNUAL EVALUATION REPORT: YEAR TWO Report Prepared by: Tiffany

### Introduction to. Hypothesis Testing CHAPTER LEARNING OBJECTIVES. 1 Identify the four steps of hypothesis testing.

Introduction to Hypothesis Testing CHAPTER 8 LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 Identify the four steps of hypothesis testing. 2 Define null hypothesis, alternative

### An Experiment in the Use of Mobile Phones for Testing at King Mongkut s Institute of Technology North Bangkok, Thailand

Page 1 of 7 An Experiment in the Use of Mobile Phones for Testing at King Mongkut s Institute of Technology North Bangkok, Thailand Krismant Whattananarong Department of Technological Education Faculty

### Name: Date: Use the following to answer questions 3-4:

Name: Date: 1. Determine whether each of the following statements is true or false. A) The margin of error for a 95% confidence interval for the mean increases as the sample size increases. B) The margin

### Two-sample t-tests. - Independent samples - Pooled standard devation - The equal variance assumption

Two-sample t-tests. - Independent samples - Pooled standard devation - The equal variance assumption Last time, we used the mean of one sample to test against the hypothesis that the true mean was a particular

### Statistics Review PSY379

Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses

### t Tests in Excel The Excel Statistical Master By Mark Harmon Copyright 2011 Mark Harmon

t-tests in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com www.excelmasterseries.com

### A Self and Peer Assessment Intervention in Mathematics Content Courses for Pre-Service Elementary School Teachers Xin Ma, Richard Millman, Matt Wells

A Self and Peer Assessment Intervention in Mathematics Content Courses for Pre-Service Elementary School Teachers Xin Ma, Richard Millman, Matt Wells University of Kentucky Introduction We explored in

### Research Proposal: Evaluating the Effectiveness of Online Learning as. Opposed to Traditional Classroom Delivered Instruction. Mark R.

1 Running head: Effectiveness of Online Learning Research Proposal: Evaluating the Effectiveness of Online Learning as Opposed to Traditional Classroom Delivered Instruction Mark R. Domenic University

### Using Clicker Technology in a Clinically-based Occupational Therapy Course: Comparing Student Perceptions of Participation and Learning

Using Clicker Technology in a Clinically-based Occupational Therapy Course: Comparing Student Perceptions of Participation and Learning Thomas Mernar, PhD, OTR Acknowledgements I would like to thank The

### Computers in teaching science: To simulate or not to simulate?

Computers in teaching science: To simulate or not to simulate? Richard N. Steinberg City College of New York Phys. Ed. Res. Suppl. to Am. J. Phys. 68, S37-S41 (2) Do computer simulations help students

### The Effect of Explicit Feedback on the Use of Language Learning Strategies: The Role of Instruction

Yıl/Year: 2013 Cilt/Volume: 2 Sayı/Issue: 5 Sayfalar/Pages: 1-12 The Effect of Explicit Feedback on the Use of Language Learning Strategies: The Role of Instruction Mohammad Rahi Islamic Azad University,

### Using Interactive Demonstrations at Slovak Secondary Schools

Using Interactive Demonstrations at Slovak Secondary Schools Zuzana Mackovjaková 1 ; Zuzana Ješková 2 1 GymnáziumJ.A. Raymana, Mudroňova 20, 080 01 Prešov, Slovakia, 2 Faculty of Science, University of

### Use of Placement Tests in College Classes

By Andrew Morgan This paper was completed and submitted in partial fulfillment of the Master Teacher Program, a 2-year faculty professional development program conducted by the Center for Teaching Excellence,

### Student Preferences for Learning College Algebra in a Web Enhanced Environment

Abstract Student Preferences for Learning College Algebra in a Web Enhanced Environment Laura Pyzdrowski West Virginia University Anthony Pyzdrowski California University of Pennsylvania It is important

### Developing Learning Activites. Robert Beichner

Developing Learning Activites Robert Beichner Peek at final results... Problem solving skills developed Conceptual learning increased Retention much higher Top students benefit most Performance in later

### Progress Report Phase I Study of North Carolina Evidence-based Transition to Practice Initiative Project Foundation for Nursing Excellence

Progress Report Phase I Study of North Carolina Evidence-based Transition to Practice Initiative Project Foundation for Nursing Excellence Prepared by the NCSBN Research Department INTRODUCTION In 2006,

### Tutorial 5: Hypothesis Testing

Tutorial 5: Hypothesis Testing Rob Nicholls nicholls@mrc-lmb.cam.ac.uk MRC LMB Statistics Course 2014 Contents 1 Introduction................................ 1 2 Testing distributional assumptions....................

### Analyses of the Critical Thinking Assessment Test Results

Analyses of the Critical Thinking Assessment Test Results J. Theodore Anagnoson Professor of Political Science November, 2000 Over the period from June, 1999 through June, 2000, Prof. Garry administered

### Recall this chart that showed how most of our course would be organized:

Chapter 4 One-Way ANOVA Recall this chart that showed how most of our course would be organized: Explanatory Variable(s) Response Variable Methods Categorical Categorical Contingency Tables Categorical

### Transformative Learning: Shifts in Students Attitudes toward Physics Measured with the Colorado Learning Attitudes about Science Survey

International Journal of Humanities and Social Science Vol. 1 No. 7 [Special Issue June 2011] Transformative Learning: Shifts in Students Attitudes toward Physics Measured with the Colorado Learning Attitudes

### Confidence Intervals for the Mean. Single Sample and Paired Samples t tests

Confidence Intervals for the Mean Single Sample and Paired Samples t tests Dr. J. Kyle Roberts Southern Methodist University Simmons School of Education and Human Development Department of Teaching and

### Achievement and Satisfaction in a Computer-assisted Versus a Traditional Lecturing of an Introductory Statistics Course

Australian Journal of Basic and Applied Sciences, 3(3): 1875-1878, 2009 ISSN 1991-8178 2009, INSInet Publication Achievement and Satisfaction in a Computer-assisted Versus a Traditional Lecturing of an

### Predictor Coef StDev T P Constant 970667056 616256122 1.58 0.154 X 0.00293 0.06163 0.05 0.963. S = 0.5597 R-Sq = 0.0% R-Sq(adj) = 0.

Statistical analysis using Microsoft Excel Microsoft Excel spreadsheets have become somewhat of a standard for data storage, at least for smaller data sets. This, along with the program often being packaged

### A Study of Student Attitudes and Performance in an Online Introductory Business Statistics Class

A Study of Student Attitudes and Performance in an Online Introductory Business Statistics Class Sue B. Schou Idaho State University ABSTRACT Due to economic constraints, more and more distance education

### Understanding Confidence Intervals and Hypothesis Testing Using Excel Data Table Simulation

Understanding Confidence Intervals and Hypothesis Testing Using Excel Data Table Simulation Leslie Chandrakantha lchandra@jjay.cuny.edu Department of Mathematics & Computer Science John Jay College of

### Program Overview. This guide explains the program components and philosophy of the research-based program, Math Navigator, Common Core Edition.

Program Overview Introduction Program Structure This guide explains the program components and philosophy of the research-based program, Math Navigator, Common Core Edition. Math Navigator blends conceptual

### Mark Elliot October 2014

Final Report on the Disclosure Risk Associated with the Synthetic Data Produced by the SYLLS Team Mark Elliot October 2014 1. Background I have been asked by the SYLLS project, University of Edinburgh

### Abstract Title: Identifying and measuring factors related to student learning: the promise and pitfalls of teacher instructional logs

Abstract Title: Identifying and measuring factors related to student learning: the promise and pitfalls of teacher instructional logs MSP Project Name: Assessing Teacher Learning About Science Teaching

### The Effect of Electronic Writing Tools on Business Writing Proficiency

The Effect of Electronic Writing Tools on Business Writing Proficiency Lisa Walters and Susan McNamara Lisa M. Walters is Visiting Assistant Professor of Business Administration and Susan McNamara is Assistant

### Smith on Natural Wages and Profits (Chapters VIII and IX of The Wealth of Nations 1 ) Allin Cottrell

1 The background Smith on Natural Wages and Profits (Chapters VIII and IX of The Wealth of Nations 1 ) Allin Cottrell When Smith begins work on the specifics of natural wages and profits, he has already

### A Proven Approach to Stress Testing Consumer Loan Portfolios

A Proven Approach to Stress Testing Consumer Loan Portfolios Interthinx, Inc. 2013. All rights reserved. Interthinx is a registered trademark of Verisk Analytics. No part of this publication may be reproduced,

### Classroom Response Systems in Statistics Courses

Classroom Response Systems in Statistics Courses Michael B. Richman 1 Teri J. Murphy 2 Curtis C. McKnight 2 Robert A. Terry 3 1 School of Meteorology, University of Oklahoma 2 Department of Mathematics,

### IMPLEMENTATION NOTE. Validating Risk Rating Systems at IRB Institutions

IMPLEMENTATION NOTE Subject: Category: Capital No: A-1 Date: January 2006 I. Introduction The term rating system comprises all of the methods, processes, controls, data collection and IT systems that support

### EXPLORATORY DATA ANALYSIS USING THE BASELINE STUDY OF THE KHANYISA EDUCATION SUPPORT PROGRAMME

EXPLORATORY DATA ANALYSIS USING THE BASELINE STUDY OF THE KHANYISA EDUCATION SUPPORT PROGRAMME Charles Simkins and Carla Pereira 1 Introduction The Khanyisa Education Support Programme aims to facilitate

### Student s Attitude of Accounting as a Profession: Can the Video "Takin' Care of Business" Make a Difference? INTRODUCTION

Student s Attitude of Accounting as a Profession: Can the Video "Takin' Care of Business" Make a Difference? INTRODUCTION Does the AICPA s video Takin' Care of Business market the accounting profession

### Alternative Online Pedagogical Models With Identical Contents: A Comparison of Two University-Level Course

The Journal of Interactive Online Learning Volume 2, Number 1, Summer 2003 www.ncolr.org ISSN: 1541-4914 Alternative Online Pedagogical Models With Identical Contents: A Comparison of Two University-Level

### Can Web Courses Replace the Classroom in Principles of Microeconomics? By Byron W. Brown and Carl E. Liedholm*

Can Web Courses Replace the Classroom in Principles of Microeconomics? By Byron W. Brown and Carl E. Liedholm* The proliferation of economics courses offered partly or completely on-line (Katz and Becker,

### The Physics First movement teaching a true

Effectiveness of Ninth-Grade Physics in Maine: Conceptual Understanding Michael J. O'Brien, Kennebunk High School, Kennebunk, ME John R. Thompson, The University of Maine, Orono, ME The Physics First movement

### The Effect of Treatment Diffusion on Educational Experimental Designs

The Effect of Treatment Diffusion on Educational Experimental Designs By Danga, Luka Amos Bauchi State Ministry of Education & Katrina A. Korb Faculty of Education University Of Jos Danga, L. A., & Korb,

### The five questions you need to ask before selecting a Business Intelligence Vendor

The five questions you need to ask before selecting a Business Intelligence Vendor Overview Over the last decade, Business Intelligence (BI) has been at or near the top of the list of many executive and

### Analysis of Student Retention Rates and Performance Among Fall, 2013 Alternate College Option (ACO) Students

1 Analysis of Student Retention Rates and Performance Among Fall, 2013 Alternate College Option (ACO) Students A Study Commissioned by the Persistence Committee Policy Working Group prepared by Jeffrey

### Measuring the response of students to assessment: the Assessment Experience Questionnaire

11 th Improving Student Learning Symposium, 2003 Measuring the response of students to assessment: the Assessment Experience Questionnaire Graham Gibbs and Claire Simpson, Open University Abstract A review

### Sociology 6Z03 Topic 15: Statistical Inference for Means

Sociology 6Z03 Topic 15: Statistical Inference for Means John Fox McMaster University Fall 2016 John Fox (McMaster University) Soc 6Z03: Statistical Inference for Means Fall 2016 1 / 41 Outline: Statistical

### The Gravity Model: Derivation and Calibration

The Gravity Model: Derivation and Calibration Philip A. Viton October 28, 2014 Philip A. Viton CRP/CE 5700 () Gravity Model October 28, 2014 1 / 66 Introduction We turn now to the Gravity Model of trip

### The initial knowledge state of college physics students

Published in: Am. J. Phys. 53 (11), November 1985, pp 1043-1048. The initial knowledge state of college physics students Ibrahim Abou Halloun a) and David Hestenes Department of Physics, Arizona State

### Research Findings on the Transition to Algebra series

Research Findings on the Transition to Algebra series Pre- and Post-Test Findings Teacher Surveys Teacher Focus Group Interviews Student Focus Group Interviews End-of-Year Student Survey The following

### Comparing Two Populations OPRE 6301

Comparing Two Populations OPRE 6301 Introduction... In many applications, we are interested in hypotheses concerning differences between the means of two populations. For example, we may wish to decide

### Local outlier detection in data forensics: data mining approach to flag unusual schools

Local outlier detection in data forensics: data mining approach to flag unusual schools Mayuko Simon Data Recognition Corporation Paper presented at the 2012 Conference on Statistical Detection of Potential

### AP Statistics 2002 Scoring Guidelines

AP Statistics 2002 Scoring Guidelines The materials included in these files are intended for use by AP teachers for course and exam preparation in the classroom; permission for any other use must be sought

### An analysis of the 2003 HEFCE national student survey pilot data.

An analysis of the 2003 HEFCE national student survey pilot data. by Harvey Goldstein Institute of Education, University of London h.goldstein@ioe.ac.uk Abstract The summary report produced from the first

### Through Proper Testing Techniques. By Perry D. Drake

Through Proper Testing Techniques By Perry D. Drake Seminar Objectives In this lunch session we will discuss how a marketer utilizes a database to ensure maximum return on investment via proper test design

### Jewish Members of Jewish Community Centers in the United States: Jewish Households and Individuals

Jewish Members of Jewish Community Centers in the United States: Jewish Households and Individuals The NJPS 2000-01 and the JCC Movement: Briefing Paper Number 1 Professor Steven M. Cohen Director, Florence

### 10. Analysis of Longitudinal Studies Repeat-measures analysis

Research Methods II 99 10. Analysis of Longitudinal Studies Repeat-measures analysis This chapter builds on the concepts and methods described in Chapters 7 and 8 of Mother and Child Health: Research methods.

### Drawing Inferences about Instructors: The Inter-Class Reliability of Student Ratings of Instruction

OEA Report 00-02 Drawing Inferences about Instructors: The Inter-Class Reliability of Student Ratings of Instruction Gerald M. Gillmore February, 2000 OVERVIEW The question addressed in this report is

### 9. Sampling Distributions

9. Sampling Distributions Prerequisites none A. Introduction B. Sampling Distribution of the Mean C. Sampling Distribution of Difference Between Means D. Sampling Distribution of Pearson's r E. Sampling

### Engineering Cultures: Online versus In-class

Engineering Cultures: Online versus In-class Rosamond Parkhurst Iver C. Ranum High School RParkhurst@adams50.org Juan Lucena Colorado School of Mines Golden, CO USA jlucena@mines.edu Barbara M. Moskal

### A physics department s role in preparing physics teachers: The Colorado Learning Assistant model

A physics department s role in preparing physics teachers: The Colorado Learning Assistant model Valerie Otero, Steven Pollock, and Noah Finkelstein University of Colorado, Boulder, Colorado 80309 In response

### Course Descriptions. Seminar in Organizational Behavior II

Course Descriptions B55 MKT 670 Seminar in Marketing Management This course is an advanced seminar of doctoral level standing. The course is aimed at students pursuing a degree in business, economics or

### Teaching Basic Accounting to Engineering Economy Students: Are Computer Tutorials More Effective than Traditional Classroom Lectures?

Teaching Basic Accounting to Engineering Economy Students: Are Computer Tutorials More Effective than Traditional Classroom Lectures? Donald N. Merino, Ph.D. P.E., and Kate D. Abel, Ph.D. Stevens Institute

### 3. Nonparametric methods

3. Nonparametric methods If the probability distributions of the statistical variables are unknown or are not as required (e.g. normality assumption violated), then we may still apply nonparametric tests

### Battleships Searching Algorithms

Activity 6 Battleships Searching Algorithms Summary Computers are often required to find information in large collections of data. They need to develop quick and efficient ways of doing this. This activity

### (Refer Slide Time: 01.26)

Discrete Mathematical Structures Dr. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras Lecture # 27 Pigeonhole Principle In the next few lectures

### Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures

Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 35487-0348 Phone:

### 2 2000 CBMS Survey of Undergraduate Programs

Chapter Summary of CBMS Findings on Mathematical Sciences Enrollment, Bachelors Degrees, Faculty, and the Curriculum in Twoand Four-Year Colleges and Universities A. What Do the CBMS Surveys Study? Every

### Unit 27: Comparing Two Means

Unit 27: Comparing Two Means Prerequisites Students should have experience with one-sample t-procedures before they begin this unit. That material is covered in Unit 26, Small Sample Inference for One

### COMPARISON OF INTERNET AND TRADITIONAL CLASSROOM INSTRUCTION IN A CONSUMER ECONOMICS COURSE

Journal of Family and Consumer Sciences Education, Vol. 20, No. 2, Fall/Winter 2002 COMPARISON OF INTERNET AND TRADITIONAL CLASSROOM INSTRUCTION IN A CONSUMER ECONOMICS COURSE Debbie Johnson Southeastern

### January 2010 Report No. 10-07

January 2010 Report No. 10-07 Youth Entering the State s Juvenile Justice Programs Have Substantial Educational Deficits; Available Data Is Insufficient to Assess Learning Gains of Students at a glance

### UBS Global Asset Management has

IIJ-130-STAUB.qxp 4/17/08 4:45 PM Page 1 RENATO STAUB is a senior assest allocation and risk analyst at UBS Global Asset Management in Zurich. renato.staub@ubs.com Deploying Alpha: A Strategy to Capture

### Chi Square Tests. Chapter 10. 10.1 Introduction

Contents 10 Chi Square Tests 703 10.1 Introduction............................ 703 10.2 The Chi Square Distribution.................. 704 10.3 Goodness of Fit Test....................... 709 10.4 Chi Square

### Running Head: COMPARISON OF ONLINE STUDENTS TO TRADITIONAL 1. The Comparison of Online Students Education to the

Running Head: COMPARISON OF ONLINE STUDENTS TO TRADITIONAL 1 The Comparison of Online Students Education to the Traditional Students of Classroom Education Brent Alan Neumeier The University of Arkansas

### CHARTER SCHOOL PERFORMANCE IN PENNSYLVANIA. credo.stanford.edu

CHARTER SCHOOL PERFORMANCE IN PENNSYLVANIA credo.stanford.edu April 2011 TABLE OF CONTENTS INTRODUCTION... 3 DISTRIBUTION OF CHARTER SCHOOL PERFORMANCE IN PENNSYLVANIA... 7 CHARTER SCHOOL IMPACT BY DELIVERY

### Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are

### 4.1 Can adult education reduce inequalities in health - Tarani Chandola, ICLS & Manchester University

ICLS Occasional Paper Series: Paper No. 4.1 Education, Education, Education: A life course perspective Four short policy relevant presentations examined education, across the life course, from adult education

### WISE Power Tutorial All Exercises

ame Date Class WISE Power Tutorial All Exercises Power: The B.E.A.. Mnemonic Four interrelated features of power can be summarized using BEA B Beta Error (Power = 1 Beta Error): Beta error (or Type II

### Experimental Designs. Y520 Strategies for Educational Inquiry

Experimental Designs Y520 Strategies for Educational Inquiry Experimental designs-1 Research Methodology Is concerned with how the design is implemented and how the research is carried out. The methodology

### Secondary school physics availability in an urban setting: Issues related to academic achievement and course offerings

Secondary school physics availability in an urban setting: Issues related to academic achievement and course offerings Angela M. Kelly Graduate Program in Science Education, Lehman College, City University